Abstract
Multi-omics data is used in oncology to improve cure rates, decrease mortality, and prevent disease progression. However, the heterogeneity, high dimensionality, and small sample sizes of this data pose challenges for modeling tumor-gene relationships. Current research focuses on inter-omics fusion, neglecting intra-omics fusion, which results in inadequate feature representation. Dimension reduction is also important to avoid overfitting due to the high dimensionality and limited samples in omics data fusion. Thus, we proposed a novel sparse fusion representation method based on VAE-NN network and applied it into GTCN to constitute a new model named MOVNG for pan-cancer classification and biomarker identification. The presented method implements inter- and intra-omics data fusion in high-level feature space. At the same time, it can be used for a universal integration framework in which different data have the traits of heterogeneity. Extensive experiments were conducted to our combined model. From it, we can know that the sparse fusion representation method has a strong ability of expression and the model achieved an average accuracy of 92.06 ± 1.50% in 33 types of tumor classification tasks. Finally, the model also identified some vital biomarkers based on the characteristics of pan-cancer.
☆ The source code at https://github.com/cx-333/MOVNG.
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Notes
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The full name of abbreviations in the figure can be found on the website (https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations).
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Chen, X., Tie, Y., Liu, F., Zhang, D., Qi, L. (2023). MOVNG: Applied a Novel Sparse Fusion Representation into GTCN for Pan-Cancer Classification and Biomarker Identification. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_52
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